# coding=utf-8
import os
# os.environ["PATH"] += os.pathsep + 'C:/Program Files (x86)/Graphviz2.38/bin/'
from keras.models import Model
from keras.layers import Input, Dense, Dropout, BatchNormalization, Conv2D, MaxPooling2D, AveragePooling2D, concatenate, \
Activation, ZeroPadding2D
from keras.layers import add, Flatten
from keras.utils import plot_model
from keras.metrics import top_k_categorical_accuracy
from keras.preprocessing.image import ImageDataGenerator
from keras.models import load_model
import os
# Global Constants
NB_CLASS=2
IM_WIDTH=224
IM_HEIGHT=224
train_root=r'/images/window/train'
vaildation_root=r'/images/window/vaild'
test_root=r'/images/window/predict'
batch_size=32
EPOCH=20#60
# train data
train_datagen = ImageDataGenerator(
width_shift_range=0.1,
height_shift_range=0.1,
shear_range=0.1,
zoom_range=0.1,
horizontal_flip=True,
rescale=1./255
)
train_generator = train_datagen.flow_from_directory(
train_root,
target_size=(IM_WIDTH, IM_HEIGHT),
batch_size=batch_size,
shuffle=True
)
# vaild data
vaild_datagen = ImageDataGenerator(
width_shift_range=0.1,
height_shift_range=0.1,
shear_range=0.1,
zoom_range=0.1,
horizontal_flip=True,
rescale=1./255
)
vaild_generator = train_datagen.flow_from_directory(
vaildation_root,
target_size=(IM_WIDTH, IM_HEIGHT),
batch_size=batch_size,
)
# test data
test_datagen = ImageDataGenerator(
rescale=1./255
)
test_generator = train_datagen.flow_from_directory(
test_root,
target_size=(IM_WIDTH, IM_HEIGHT),
batch_size=batch_size,
)
def Conv2d_BN(x, nb_filter, kernel_size, strides=(1, 1), padding='same', name=None):
if name is not None:
bn_name = name + '_bn'
conv_name = name + '_conv'
else:
bn_name = None
conv_name = None
x = Conv2D(nb_filter, kernel_size, padding=padding, strides=strides, activation='relu', name=conv_name)(x)
x = BatchNormalization(axis=3, name=bn_name)(x)
return x
def identity_Block(inpt, nb_filter, kernel_size, strides=(1, 1), with_conv_shortcut=False):
x = Conv2d_BN(inpt, nb_filter=nb_filter, kernel_size=kernel_size, strides=strides, padding='same')
x = Conv2d_BN(x, nb_filter=nb_filter, kernel_size=kernel_size, padding='same')
if with_conv_shortcut:
shortcut = Conv2d_BN(inpt, nb_filter=nb_filter, strides=strides, kernel_size=kernel_size)
x = add([x, shortcut])
return x
else:
x = add([x, inpt])
return x
def bottleneck_Block(inpt,nb_filters,strides=(1,1),with_conv_shortcut=False):
k1,k2,k3=nb_filters
x = Conv2d_BN(inpt, nb_filter=k1, kernel_size=1, strides=strides, padding='same')
x = Conv2d_BN(x, nb_filter=k2, kernel_size=3, padding='same')
x = Conv2d_BN(x, nb_filter=k3, kernel_size=1, padding='same')
if with_conv_shortcut:
shortcut = Conv2d_BN(inpt, nb_filter=k3, strides=strides, kernel_size=1)
x = add([x, shortcut])
return x
else:
x = add([x, inpt])
return x
if __name__ == '__main__':
if os.path.exists('resnet_34.h5'):
model=load_model('resnet_34.h5')
else:
model=check_print()
model.fit_generator(train_generator,validation_data=vaild_generator,epochs=EPOCH,steps_per_epoch=train_generator.n/batch_size
,validation_steps=vaild_generator.n/batch_size)
model.save('resnet_34.h5')
loss,acc,top_acc=model.evaluate_generator(test_generator, steps=test_generator.n / batch_size)
print ('Test result:loss:%f,acc:%f,top_acc:%f' % (loss, acc, top_acc))